Deep Learning for Chinese Language Sentiment Extraction and Analysis

Zhu, Zhu

doi:10.1155/2022/8145445

Cited by 6 publications

(5 citation statements)

References 13 publications

(15 reference statements)

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“…In the case of Chinese word sentiment extraction and word segmentation, operation is extremely tedious as it involves the inclusion of multiple implicit and explicit words. The study in [27] uses expressions and sentiment vocabulary dictionaries in combination with hybrid structures to develop information synergy methods that enable sentimental analysis. The study in [28] discusses the role of an intelligent teaching system which involves the use of network learning and network intelligent teaching platforms.…”

Section: Expression Methods Of Positionmentioning

confidence: 99%

A Typological Examination of Modern Chinese OV Word Order Based on Deep Learning and the Design of Stance Expression Methods

Zhang

2022

Wireless Communications and Mobile Computing

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Object-verb (OV) inflections are an important grammatical device in Chinese with denotative utility. The decorativeness of OV inflections shows different levels in Chinese: OV nouns denote things in reality and are the most denotative; OV independent structures can denote both denotation and trait; OV structures that are definite are not self-sufficient and denote a certain trait. The verb category of V is worn out in denotative OV structures, and OV phrases must repair the wear and tear of V’s category and enhance V’s declarativity when forming small sentences. In this paper, we propose an attention-based approach to Chinese stance representation based on modern Chinese OV order types; firstly, we use bidirectional (bidirectional) long and short-term memory neural networks (LSTM) and convolutional neural networks (CNNs) to obtain text representation vectors and local convolutional features, respectively, and then, we use attention mechanisms to add influence weight information to the local convolutional features and finally fuse the two features for classification. Experiments on the relevant corpus show that the method achieves better stance representation results, and the addition of attention mechanisms can effectively improve the accuracy of stance representation.

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Section: Expression Methods Of Positionmentioning

confidence: 99%

A Typological Examination of Modern Chinese OV Word Order Based on Deep Learning and the Design of Stance Expression Methods

Zhang

2022

Wireless Communications and Mobile Computing

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“…Zhu 2022 et al Computer sentiment analysis faces challenges due to Chinese word segmentation complexity and implicitexplicit word pairings. Employing phrase dictionaries and hybrid structures improves sentiment analysis effectiveness (12) .…”

Section: Literature Reviewmentioning

confidence: 99%

Synthesis of Slogans with Predicted Sentiment from Twitter using a Novel Hybrid SDG-LSTM Model for Election Campaigns

Sangle,

Sedamkar

2024

IJST

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Objectives: The primary objectives of this study encompass the enhancement of election campaign strategies through the synthesis of sentiment-laden slogans derived from Twitter data. This is achieved by employing a novel Hybrid SDG-LSTM model, aiming to improve sentiment prediction accuracy and communication efficacy in the context of political campaigns. Methods: The process of slogan generation relies on sentiment prediction derived from sentiment-laden tweets. The proposed sentiment analysis methods for election campaign slogans encompass Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). A novel approach is introduced through the Hybrid SDG-LSTM model, leveraging the combination of Self-Distillation Guidance (SDG) with LSTM to enhance sentiment prediction accuracy and efficiency. This innovative method aims to provide a more robust and effective means of analyzing and generating slogans for election campaigns. Findings: The performance assessment of Deep Learning models, GRU, LSTM, and the Hybrid architecture, unveiled compelling outcomes. GRU showcased a commendable accuracy of 92.98%, while LSTM impressed with 95.91%. Remarkably, the Hybrid Spatial LSTM with GRU surpassed both, achieving perfection with 100% accuracy, precision, recall, and an exceptionally low loss of 0.0. These results underscore the superior performance and efficacy of the Hybrid model in sentiment analysis tasks. Novelty: The novelty of this research is encapsulated in the introduction of the Hybrid Spatial LSTM with GRU model, which demonstrates groundbreaking 100% accuracy, surpassing current models. This innovation capitalizes on the synergistic fusion of spatial attention mechanisms and the dynamic nature of GRU, marking a substantial advancement and establishing a new benchmark for highly accurate predictions in the domain of sentiment analysis. Keywords: Slogan Generation, Sentiment Analysis, Election Campaign, Deep Learning, LSTM, GRU

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“…e problem of sparsity in short text can be solved by modifying LJST and introducing BI-LJST [21]. e CLSTM model combined with CNN and LSTM greatly improves the efficiency of lexical classification and analysis [22]. However, the mass message in the government system is characterized by high complexity and wide distribution of the public text message, so the simple short text classification model cannot be well applied to such scenarios.…”

Section: Literature Reviewmentioning

confidence: 99%

The Big Data Analysis and Visualization of Mass Messages under “Smart Government Affairs” Based on Text Mining

Wang

Guo

2022

Mathematical Problems in Engineering

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Based on the records of people’s political inquiries, comments from public sources on the Internet and the data of the relevant departments’ responses to some people’s messages, this study uses text analysis, text feature extraction, model building, text mining, and other evaluation methods to study and evaluate the three aspects of government services: analysis of public comments, mining of hot issues and evaluation of replies, which aims to prompt the government to understand the needs of the people quickly and solve the relevant problems in a timely and effective manner. The results show that the final classification accuracy using BERT is 3.4% and 1.8% higher than that using TF-IDF and Word2vec, respectively. Multi-classification of message data was realized by BERT combined with the LinearSVC algorithm, and the crowd message was accurately divided into seven types of problems, with an accuracy of 96.7%. It is intended to be transferred to relevant departments for processing. For problems related to people’s livelihood, law, economy, and other aspects, different departments should take countermeasures to solve them and achieve systematic, departmental, and regional coordination. This will enhance the ability of government platforms to deal with problems. Through the definition of hot indexes, hot issues mining can timely find the outstanding problems reflected by the masses. At the same time, the feedback evaluation system can comprehensively evaluate the work of relevant departments from the perspectives of relevance, completeness, and interpretability. Big data analysis technology based on text mining is a feasible way to solve the difficulties of text data analysis. The analysis model constructed in this study is suitable for mining and analyzing unstructured data with short text features, and the results can provide guidance for government decision-making.

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Deep Learning for Chinese Language Sentiment Extraction and Analysis

Cited by 6 publications

References 13 publications

A Typological Examination of Modern Chinese OV Word Order Based on Deep Learning and the Design of Stance Expression Methods

A Typological Examination of Modern Chinese OV Word Order Based on Deep Learning and the Design of Stance Expression Methods

Synthesis of Slogans with Predicted Sentiment from Twitter using a Novel Hybrid SDG-LSTM Model for Election Campaigns

The Big Data Analysis and Visualization of Mass Messages under “Smart Government Affairs” Based on Text Mining

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